Digital cameras are used by a growing number of consumer and professional photographers. These cameras use one or more CCD or CMOS image sensors to capture images, and digitally process these captured images to produce digital image files, which are stored in a digital memory in the camera.
Prior art Kodak digital cameras use “smart scene” modes to automatically identify a type of scene (e.g. sports, portrait) and to then automatically select an appropriate exposure program for the identified scene type. The exposure program normally sets the f/number, exposure time, and ISO speed used when the image is taken.
It is known that the image processing used in a digital camera can adaptively adjust the signal values according to the dynamic range of the input image. For example, WO2006018658 “Image Processing Method and Computer Software for Image Processing” assigned to Apical, Ltd., teaches image correction processing which comprises altering area image intensity values of an image according to a dynamic range compression image transform. While the patent teaches that this image processing can be used in a digital camera, there is no suggestion that the exposure level of the image sensor in the camera be set based on the type of scene being captured.
It is known that a digital camera can provide image processing to improve images that appear to be underexposed due to difficult lighting situations or insufficient flash. For example, recent Nikon CoolPix cameras include a “D-Lighting” function to fix problems that occur with excessive backlighting and underexposed images. D-Lighting, selected by the user during playback mode, automatically modifies the image to compensate for insufficient flash or excessive back lighting. The camera saves the original image and the corrected copy to the camera's internal or removable memory.
It is known that a digital camera can capture images using a reduced exposure level (e.g. a high ISO setting) in order to preserve more of the image highlights. For example, some Canon DSLR cameras include a “highlight tone priority” (HTP) setting, which allows the camera to utilize the much greater headroom available in the sensor pixels when shooting at elevated ISO settings to recover highlight detail that would otherwise be lost.
It is known that digital images, including digital images captured by a digital camera, can be digitally processed to adjust neutral density balance and color balance. In particular, adaptive neutral density balance adjustment processing can be performed, as described in commonly assigned U.S. Pat. No. 6,243,133 titled “Method for Automatic Scene Balance of Digital Images” to Spaulding, Gindele and Niederbaumer, the disclosure of which is incorporated herein by reference. Automatic color balance can be performed, as described in commonly assigned U.S. Pat. No. 6,573,932 titled “Method for Automatic White Balance of Digital Images” to Adams, Hamilton, Gindele and Pillman, the disclosure of which is incorporated herein by reference. These examples are not limiting, and many other neutral density and color balance adjustment processing solutions may be used.
It is known that digital images, including digital images captured by a digital camera, can be digitally processed to compensate for the presence of flare light. In particular, flare compensation processing can be performed, as described in commonly assigned U.S. Pat. No. 6,912,321 titled “Method of Compensating a Digital Image for the Effects of Flare Light” to Gindele, the disclosure of which is incorporated herein by reference. This example is not limiting, and many other flare compensation processing solutions may be used.
It is known that digital images, including digital images captured by a digital camera, can be digitally processed to compensate for the dynamic range of the scene. In particular, adaptive tone scale adjustment processing can be performed, as described in commonly assigned U.S. Pat. No. 6,937,775 titled “Method of Enhancing the Tone Scale of a Digital Image to Extend the Linear Response Range Without Amplifying Noise” to Gindele and Gallagher, U.S. Pat. No. 7,113,649 titled “Enhancing the Tonal Characteristics of Digital Images” to Gindele, U.S. Pat. No. 7,130,485 titled “Enhancing the Tonal and Color Characteristics of Digital Images Using Expansive and Compressive Tone Scale Functions” to Gindele and Gallagher, U.S. Pat. No. 7,058,234 titled “Enhancing the Tonal, Spatial, and Color Characteristics of Digital Images Using Expansive and Compressive Tone Scale Functions” to Gindele and Gallagher, and U.S. Pat. No. 7,043,090 titled “Enhancing the Tonal Characteristics of Digital Images Using Expansive and Compressive Tone Scale Functions” to Gindele and Gallagher, and commonly assigned U.S. Patent Publication No. US20040096103, filed on Nov. 14, 2002 titled “Method of Spatially Filtering a Digital Image Using Chrominance Information” to Gallagher and Gindele, and U.S. Patent Publication No. US20040057632, filed on Sep. 19, 2002 titled “Enhancing the Tonal Characteristics of Digital Images Using Inflection Points in a Tone Scale Function” to Gindele the disclosures of which are incorporated by reference herein.
It is known that gray level correction can be used to correct the brightness and contrast of an image which is captured under an illumination condition where the subject is photographed alongside a bright light source. Gamma correction and histogram correction are typical examples of the gray level correction that can be used to correct such images. With gamma correction and histogram correction, however, because the image correction is performed using a fixed coefficient, problems may arise where the image is clipped white due to overexposure or in the case of under exposure, clipped black or obscured by noise
It is known that adaptive gray level correction (adaptive enhancement) can be used to correct images, where the gray level values of pixels adjacent to a pixel to be corrected are used to determine correction coefficients. With this approach, correction which adapts to the content of an image can be achieved. An example of adaptive gray level correction is disclosed in “Comparison of Retinex Models for Hardware Implementation” by Nosato et al., IEICE technical report, SIS, 2005-16, pp. 19-24 (June, 2005). This adaptive gray level correction is based on Retinex theory, which assumes that an input image is represented by a product of illumination light and reflectivity. Illumination light is separated from an input image to thereby obtain a reflectivity image as a correction image. Given that an input image I is equal to an illumination light L times a reflectivity R (correction image), the relationship of R (x, y)=exp{log(I(x,y))−log(L(x,y))} can be achieved. Calculus of variation is used to estimate the illumination light, and a plurality of layers k with a resolution which is ½k that of the original image are generated. Calculations for updating the illumination light are repeated, starting from a layer with a lower resolution. Here, the calculation for updating the illumination light is performed using the expression L(x, y)=L(x, y)−μNSD×G(x, y), wherein G(x, y) is a gradient of cost function and μNSD is a learning coefficient. Specifically, a processing, in which G(x, y) is first calculated, and μNSD (x, y) is then calculated, and based on these calculation results, L(x, y) is calculated, is repeated.
Further, JP2007-27967A discloses that, when a portrait photographing mode is selected by the photographer, an image is captured with the exposure value being set to a value less than the exposure value normally computed by an AE (Automatic Exposure) detector, and gray level correction is applied to the image data from the image sensor by using a gamma transform table for increasing the dynamic range of image data which has been subjected to gray level conversion processing, thereby correcting the brightness value of portions of the image with insufficient brightness which are located in the vicinity of the center of the subject. Note that the portrait photographing mode must be manually selected by the photographer, rather than being automatically determined by the camera by analyzing preview image data.